Prognostic value of immune-related genes in clear cell renal cell carcinoma

Predictive three-biomarker panel in peripheral blood mononuclear cells for detecting hepatocellular carcinoma

Hepatocellular carcinoma (HCC) ranks among the most prevalent cancers and accounts for a significant proportion of cancer-associated deaths worldwide. This disease, marked by multifaceted etiology, often poses diagnostic challenges. Finding a reliable and non-invasive diagnostic method seems to be necessary. In this study, we analyzed the gene expression profiles of 20 HCC patients, 12 individuals with chronic hepatitis, and 15 healthy controls. Enrichment analysis revealed that platelet aggregation, secretory granule lumen, and G-protein-coupled purinergic nucleotide receptor activity were common biological processes, cellular components, and molecular function in HCC and chronic hepatitis B (CHB) compared to healthy controls, respectively. Furthermore, pathway analysis demonstrated that "estrogen response" was involved in the pathogenesis of HCC and CHB conditions, while, "apoptosis" and "coagulation" pathways were specific for HCC. Employing computational feature selection and logistic regression classification, we identified candidate genes pivotal for diagnostic panel development and evaluated the performance of these panels. Subsequent machine learning evaluations assessed these panels' performance in an independent cohort. Remarkably, a 3-marker panel, comprising RANSE2, TNF-α, and MAP3K7, demonstrated the best performance in qRT-PCR-validated experimental data, achieving 98.4% accuracy and an area under the curve of 1. Our findings highlight this panel's promising potential as a non-invasive approach not only for detecting HCC but also for distinguishing HCC from CHB patients.

Development and implementation of a prognostic model for clear cell renal cell carcinoma based on heterogeneous TLR4 expression

Objective

Clear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cell carcinomas and has the worst prognosis, originating from renal tubular epithelial cells. Toll-like receptor 4 (TLR4) plays a crucial role in ccRCC proliferation, infiltration, and metastasis. The aim of this study was to construct a prognostic scoring model for ccRCC based on TLR4 expression heterogeneity and to explore its association with immune infiltration, thereby providing insights for the treatment and prognostic evaluation of ccRCC.

Methods

Using R software, a differential analysis was conducted on normal samples and ccRCC samples, and in conjunction with the KEGG database, a correlation analysis for the clear cell renal cell carcinoma pathway (hsa05211) was carried out. We observed the expression heterogeneity of TLR4 in the TCGA-KIRC cohort and identified its related differential genes (TRGs). Based on the expression levels of TRGs, consensus clustering was employed to identify TLR4-related subtypes, and further clustering heatmaps, principal component, and single-sample gene set enrichment analyses were conducted. Overlapping differential genes (ODEGs) between subtypes were analysed, and combined with survival data, univariate Cox regression, LASSO, and multivariate Cox regression were used to establish a prognostic risk model for ccRCC. This model was subsequently evaluated through ROC analysis, risk factor correlation analysis, independent prognostic factor analysis, and intergroup differential analysis. The ssGSEA model was employed to explore immune heterogeneity in ccRCC, and the performance of the model in predicting patient prognosis was evaluated using box plots and the oncoPredict software package.

Results

In the TCGA-KIRC cohort, TLR4 expression was notably elevated in ccRCC samples compared to normal samples, correlating with improved survival in the high-expression group. The study identified distinct TLR4-related differential genes and categorized ccRCC into three subtypes with varied survival outcomes. A risk prognosis model based on overlapping differential genes was established, showing significant associations with immune cell infiltration and key immune checkpoints (PD-1, PD-L1, CTLA4). Additionally, drug sensitivity differences were observed between risk groups.

Conclusion

In the TCGA-KIRC cohort, the expression of TLR4 in ccRCC samples exhibited significant heterogeneity. Through clustering analysis, we identified that the primary immune cells across subtypes are myeloid-derived suppressor cells, central memory CD4 T cells, and regulatory T cells. Furthermore, we successfully constructed a prognostic risk model for ccRCC composed of 17 genes. This model provides valuable references for the prognosis prediction and treatment of ccRCC patients.

A model based on immunogenic cell death-related genes predicts prognosis and response to immunotherapy in kidney renal clear cell carcinoma

Background

The prognosis of patients with kidney renal clear cell carcinoma (KIRC), a life-threatening condition, is poor. Immunogenic cell death (ICD) induces regulated cell death via immunogenic signal secretion and exposure. ICD induces regulated cell death through immunogenic signal secretion and exposure. ICD plays an essential role in tumorigenesis, however, the role of ICD in KIRC remains unclear.

Methods

This study examined the expression levels of 34 ICD-related genes in The Cancer Genome Atlas (TCGA) data set. Signature genes linked to KIRC survival were identified using Cox regression. Next, a prognostic risk model (RM) was built. Subsequently, the KIRC patients were divided into low- and high-risk groups. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were plotted. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were carried out to investigate the possible role of differential gene expression between the two groups. The immune microenvironment (IME) was assessed using Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression, CIBERSORT, and single-sample gene-set enrichment analysis algorithms. An enrichment analysis was used to determine the biological significance of these regulatory networks we conducted. The relationship between immune checkpoint gene expression and risk score, and the relationship between treatment outcome and gene expression were assessed using correlation analyses.

Results

We developed a KIRC RM based on five ICD-related genes (i.e., FOXP3, IFNB1, IL6, LY96, and TLR4), which were identified as the prognostic signature genes. Using the TCGA data set, we conducted a survival analysis and found that the 3-year RM had an area under the curve (AUC) of 0.735, which validated the reliability of the signature. Similarly, using the International Cancer Genome Consortium (ICGC) data set, we found that the 3-year RM had an AUC of 0.732.

Conclusions

A RM based on five ICD-related genes was built to predict the prognosis of KIRC patients. This RM predicted patient prognosis and reflected the tumor IME of KIRC patients. Thus, this RM could be used to promote individualized treatments and provide potential novel targets for immunotherapy.

A novel prognostic predictor of immune microenvironment and therapeutic response in clear cell renal cell carcinoma based on angiogenesis-immune-related gene signature

Background

Clear cell renal cell carcinoma (ccRCC), the most common type of RCC, typically produces no symptoms initially. Patients with ccRCC are at increased risk of developing advanced metastatic disease due to the absence of dependable and effective prognostic biomarkers. Therefore, it is particularly urgent to find optimal stratification of patients with ccRCC to distinguish the clinical benefits of different malignant degrees. Angiogenesis has a profound impact on the malignant behavior of renal cancer cells, and anti-angiogenic drugs have been applied to metastatic renal cancer patients. Moreover, immune function dysregulation is also a significant factor in tumorigenesis. We aim to construct a predictive model that combines angiogenesis and immune-related genes (AIRGs) to aid clinicians in predicting ccRCC prognosis.

Methods

We gathered transcriptome and clinicopathology data from two datasets, the E-MTAB-1980 dataset and the Cancer Genome Atlas (TCGA). We utilized consensus clustering to find new molecular subgroups. A predictive model for the prognosis of angiogenesis-immune-associated genes (AIRGs) was conducted by the lasso and multivariate Cox regression analysis. The signature's predictive ability was then tested in different datasets. Meticulous scrutiny and comprehensive assessment were undertaken, both internally and externally, to establish the prognostic model. Analyses of immunogenomics were carried out to examine the relationship between risk scores and clinical/immune features, including immune cell infiltration, genomic alterations, and response to targeted and immunotherapy therapy.

Results

Our prognostic signature, comprising 4 AIRGs, stood as an independent prognostic factor for ccRCC, while risk scores emerged as a novel indicator for forecasting overall survival. Risk scores exhibited significant associations with various immunophenotypic factors, such as oncogenic pathways, antitumor response, different immune cell infiltration, antitumor immunity, and response to targeted and immunotherapy therapy.

Conclusions

AIRGs-based prognostic prediction model could effectively predict immunotherapy responses and survival outcomes of ccRCC.

SEMA3G, downregulated by ncRNAs, correlates with favorable prognosis and tumor immune infiltration in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC), relatively aggressive subtype of renal cell carcinoma, lacks of effective targets and promising biomarkers. Recently, although the function and immune correlation of semaphorin 3G (SEMA3G) in cancer draw more and more attention, its specific role and mechanism in KIRC are still not fully understood. In this work, we firstly conducted pan-cancer expression and survival bioinformatic analysis for SEMA3G and showed that SMEA3G might be a potential tumor suppressor and favorable prognostic biomarker in KIRC. Next, upstream noncoding RNA (ncRNA) regulatory mechanism of SEMA3G in KIRC was explored. By performing a series of in silico analyses, we identified that TBX2-AS1-miR-146a/b-5p axis was partially responsible for SEMA3G downregulation in KIRC. Furthermore, we also confirmed significant correlation of SEMA3G expression with tumor immune infiltration levels, expression of biomarkers of immune cells or immune checkpoints in KIRC. Taken together, the current data elucidated that ncRNA-caused downregulation of SEMA3G markedly linked to favorable prognosis and tumor immune infiltration in KIRC.

A Listeria-based vaccine targeting ISG15 exerts anti-tumor efficacy in renal cell carcinoma

Renal cell carcinoma (RCC) is the deadliest form of urological cancer and is projected to be the fourth most common neoplasm in the USA in males by 2040. In addition to the current poor prognosis with 5-year survival rates hardly reaching 15%, the prevalence of resistance to currently available systemic therapies has also established an urgent need to develop new treatment regimen(s) for advanced RCC. Interferon-stimulated gene 15 (ISG15) is the first identified ubiquitin-like modifier and has been intensively studied for its central role in innate immunity against intracellular pathogens. However, in this study, we identified ISG15 as a novel tumor-associated antigen and prognostic marker in RCC. Further, we therapeutically targeted elevated ISG15 expression by means of a Listeria monocytogenes (Lm)-based vaccine, designated Lm-LLO-ISG15, in both subcutaneous and orthotopic RCC mouse models. Treatment with Lm-LLO-ISG15 resulted in an influx of tumor-infiltrating effector T cells and significant anti-tumor efficacy in both subcutaneous and orthotopic RCC tumor models. Treatment with Lm-LLO-ISG15 also generated a robust interferon-gamma response and attracted a larger pool of polyfunctional T cells into the tumor microenvironment. Importantly, the therapeutic efficacy of Lm-LLO-ISG15 in RCC is comparable to that of anti-PD-1 and sunitinib, the current frontline therapies for RCC patients. Collectively, our work illustrates that targeting ISG15 in RCC with a CTL-based immunotherapy such as Lm-LLO-ISG15 is a promising and potentially translatable therapeutic strategy to enhance survival in RCC patients.

ROCK2-RNA interaction map reveals multiple biological mechanisms underlying tumor progression in renal cell carcinoma

Renal cell carcinoma (RCC) is the most common form of kidney cancer in adults. Despite new therapeutic modalities, the outcomes for RCC patients remain unsatisfactory. Rho-associated coiled-coil forming protein kinase 2 (ROCK2) has previously been shown to be upregulated in RCC, and its expression was negatively correlated with patient survival. However, the precise molecular function of ROCK2 has remained unclear. Herein, using RNA-seq analysis of ROCK2 knockdown and control cells, we identified 464 differentially expressed genes, and 1287 alternative splicing events in 786-O RCC cells. Furthermore, mapping of iRIP-seq reads in 786-O cells showed a biased distribution at 5' UTR, intronic and intergenic regions. By comparing ROCK2-regulated alternative splicing and iRIP-seq data, we found 292 overlapping genes that are enriched in multiple tumorigenic pathways. Taken together, our work defined a complex ROCK2-RNA interaction map on a genomic scale in a human RCC cell line, which deepens our understanding of the molecular function of ROCK2 in cancer development.

Riskscore Model Based on Oxidative Stress-Related Genes May Facilitate the Prognosis Evaluation for Patients With Colon Cancer

INTRODUCTION

This study aims to identify the oxidative stress-related genes (OSRGs) with prognostic value and develop a riskscore model for prognosis evaluation in colon cancer.

METHODS

The transcriptome data and corresponding clinical information about colon cancer were extracted from The Cancer Genome Atlas database. Differentially expressed OSRGs and transcription factors (TFs) were identified between the normal and tumor samples. A riskscore model was established with OSRGs filtered from Cox regression analysis. This riskscore model was appraised by Kaplan-Meier plot, receiver operating characteristic curve, and Cox regression analysis. The clinical relevance of riskscore and its association with immune cell infiltration were also evaluated.

RESULTS

A total of 307 differentially expressed OSRGs and 64 differential TFs were identified. A TF-OSRG regulatory network was constructed in Cytoscape software. A riskscore model was established based on 17 OSRGs with independent prognostic value. This riskscore model could separate the patients into low-risk and high-risk groups. It also had good predictive ability, with an area under the curve = 0.8. In multivariate Cox regression analysis, age, T stage, and riskscore were identified as independent risk factors in colon cancer. Riskscore was significantly correlated with T stage, N stage, and immune cell infiltration.

DISCUSSION

We established a useful riskscore model with 17 OSRGs for prognosticating the overall survival in patients with colon cancer. This study provides a new insight into the clinical utility of OSRG-based riskscore model, which will hopefully facilitate the prognosis evaluation of patients with colon cancer.

COL7A1 Expression Improves Prognosis Prediction for Patients with Clear Cell Renal Cell Carcinoma Atop of Stage

Clear-cell renal cell carcinoma (ccRCC) accounts for 75% of kidney cancers. Due to the high recurrence rate and treatment options that come with high costs and potential side effects, a correct prognosis of patient survival is essential for the successful and effective treatment of patients. Novel biomarkers could play an important role in the assessment of the overall survival of patients. COL7A1 encodes for collagen type VII, a constituent of the basal membrane. COL7A1 is associated with survival in many cancers; however, the prognostic value of COL7A1 expression as a standalone biomarker in ccRCC has not been investigated. With five publicly available independent cohorts, we used Kaplan-Meier curves and the Cox proportional hazards model to investigate the prognostic value of COL7A1, as well as gene set enrichment analysis to investigate genes co-expressed with COL7A1. COL7A1 expression stratifies patients in terms of aggressiveness, where the 5-year survival probability of each of the four groups was 72.4%, 59.1%, 34.15%, and 8.6% in order of increasing expression. Additionally, COL7A1 expression was successfully used to further divide patients of each stage and histological grade into groups of high and low risk. Similar results were obtained in independent cohorts. In vitro knockdown of COL7A1 expression significantly affected ccRCC cells' ability to migrate, leading to the hypothesis that COL7A1 may have a role in cancer aggressiveness. To conclude, we identified COL7A1 as a new prognosis marker that can stratify ccRCC patients.

Emerging roles and mechanisms of semaphorins activity in cancer

Semaphorins are regulatory molecules that are linked to the modulation of several cancer processes, such as angiogenesis, cancer cell invasiveness and metastasis, tumor growth, as well as cancer cell survival. Semaphorin (SEMA) activity depends on the cancer histotypes and their particularities. In broad terms, the effects of SEMAs result from their interaction with specific receptors/co-receptors - Plexins, Neuropilins and Integrins - and the subsequent effects upon the downstream effectors (e.g. PI3K/AKT, MAPK/ERK). The present article serves as an integrative review work, discussing the broad implications of semaphorins in cancer, focusing on cell proliferation/survival, angiogenesis, invasion, metastasis, stemness, and chemo-resistance/response whilst highlighting their heterogeneity as a family. Herein, we emphasized that semaphorins are largely implicated in cancer progression, interacting with the tumor microenvironment components. Whilst some SEMAs (e.g. SEMA3A, SEMA3B) function widely as tumor suppressors, others (e.g. SEMA3C) act as pro-tumor semaphorins. The differences observed in terms of the biological structure of SEMAs and the particularities of each cancer histotypes require that each semaphorin be viewed as a unique entity, and its roles must be researched accordingly. A more in-depth and comprehensive view of the molecular mechanisms that promote and sustain the malignant behavior of cancer cells is of utmost importance.

MiR-146b-5p/SEMA3G regulates epithelial-mesenchymal transition in clear cell renal cell carcinoma

OBJECTIVE

The primary purpose was to unveil how the miR-146b-5p/SEMA3G axis works in clear cell renal cell carcinoma (ccRCC).

METHODS

ccRCC dataset was acquired from TCGA database, and target miRNA to be studied was further analyzed using survival analysis. We performed miRNA target gene prediction through the database, and those predicted miRNAs were intersected with differential mRNAs. After calculating the correlation between miRNAs and mRNAs, we completed the GSEA pathway enrichment analysis on mRNAs. MiRNA and mRNA expression was examined by qRT-PCR. Western blot was introduced to detect SEMA3G, MMP2, MMP9 expression, epithelial-mesenchymal transition (EMT) marker proteins, and Notch/TGF-β signaling pathway-related proteins. Targeted relationship between miRNA and mRNA was validated using a dual-luciferase test. Transwell assay was employed to assess cell migration and invasion. Wound healing assay was adopted for evaluation of migration ability. The effect of different treatments on cell morphology was observed by a microscope.

RESULTS

In ccRCC cells, miR-146b-5p was remarkably overexpressed, yet SEMA3G was markedly less expressed. MiR-146b-5p was capable of stimulating ccRCC cell invasion, migration and EMT, and promoting the transformation of ccRCC cell morphology to mesenchymal state. SEMA3G was targeted and inhibited via miR-146b-5p. MiR-146b-5p facilitated ccRCC cell migration, invasion, morphology transforming to mesenchymal state and EMT process by targeting SEMA3G and regulating Notch and TGF-β signaling pathways.

CONCLUSION

MiR-146b-5p regulated Notch and TGF-β signaling pathway by suppressing SEMA3G expression, thus promoting the growth of ccRCC cells, which provides a possible target for ccRCC therapy and prognosis prediction.

Identification of a Novel Myc-Regulated Gene Signature for Patients with Kidney Renal Clear Cell Carcinoma

Given that myc was known to be a cancer-causing gene in several cancers including kidney renal clear cell carcinoma (KIRC). We aimed to construct myc-regulated genes (MRGs)-based prognostic signature. We obtained the mRNA expression and clinical data of KIRC from The Cancer Genome Atlas (TCGA) database and MRGs from the Molecular Signature Database (MSigDB). Then, a prognostic signature consisting of 8 MRGs (IRF9, UBE2C, YBX3, CDKN2B, CKAP2L, CYFIP2, FBLN5, and PDLIM7) was developed by differential expression analysis, cox regression analysis, and least absolute shrinkage and selection operator (lasso) analysis. Patients with KIRC were divided into high- and low-risk groups based on risk scores of MRGs-based signatures. Patients in the high-risk group showed inferior clinical characteristics and survival. In addition, the risk score was an independent prognostic factor for KIRC, and the risk score=based nomogram displayed satisfactory performance to predict the survival of KIRC. The MRGs-based signature is also correlated with immune cell infiltration and the mRNA expression of important immune checkpoints (IDO2, PDCD1, LAG3, FOXP3, and TIGIT). The tumor mutation burden (TMB) landscape between the high- and low-risk groups showed higher levels of TMB in the high-risk group than in the low-risk group and that higher levels of TMB predicted a poorer prognosis in KIRC. Furthermore, patients with KIRC in the high-risk group are more likely to experience immune escape. At last, we found patients with KIRC in the high-risk group were more sensitive to several chemotherapy drugs such as sunitinib, gefitinib, nilotinib, and rapamycin than patients with KIRC in the low-risk group. Our study successfully constructed and validated an MRGs-based signature that can predict clinical characteristics, prognosis, level of immune infiltration, and responsiveness to immunotherapy and chemotherapy drugs in patients with KIRC.

A Prognostic Model of Seven Immune Genes to Predict Overall Survival in Childhood Acute Myeloid Leukemia

Background. Acute myeloid leukemia (AML) is one of the most common hematological malignancies and accounts for about 20% of childhood leukemias. Currently, immunotherapy is one of the recommended treatment schemes for recurrent AML patients to improve their survival rates. Nonetheless, low remission and high mortality rates are observed in recurrent AML and challenge the prognosis of AML patients. To address this problem, we aimed to establish and verify a reliable prognostic risk model using immune-related genes to improve the prognostic evaluation and recommendation for personalized treatment of AML. Methods. Transcriptome data and clinical data were acquired from the TARGET database while immune genes were sourced from InnateDB and ImmPort Shared databases. The mRNA expression profile matrix of immune genes from 62 normal samples and 1408 AML cases was extracted from the transcriptome data and subjected to differential expression (DE) analysis. The entire cohort of DE immune genes was randomly divided into the test group and training group. The prognostic model associated with immune genes was constructed using the training group. The test group and entire cohort were employed for model validation. Lastly, we analyzed the potential clinical application of the model and its association with immune cell infiltration. Results. In total, 751 DE immune genes were differentially regulated, including 552 upregulated and 199 downregulated. Based on these DE genes, we developed and validated a prognostic risk model composed of seven immune genes, GDF1, TPM2, IL1R1, PSMD4, IL5RA, DHCR24, and IL12RB2. This model is able to predict the 5-year survival rate more accurately compared with age, gender, and risk stratification. Further analysis showed that CD8+ T-cell contents and neutrophil infiltration decreased but macrophage infiltration increased as the risk score increased. Conclusions. A seven-immune gene model of AML was developed and validated. We propose this model as an independent prognostic variable able to estimate the 5-year survival rate. In addition, the model can also reflect the immune microenvironment of AML patients.

Cell Differentiation Trajectory Predicts Prognosis and Immunotherapeutic Response in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the main type of malignancy in kidney related to glucose metabolism. Primary single cell culture and single cell sequencing are novel research technologies. In this study, we explored the differentiation status of ccRCC cells and its significance in prognosis and immunotherapeutic response through bioinformatics. We characterized distinct differentiation states and differentiation-related genes (DRGs) in ccRCC cells through single cell RNA sequencing (scRNA-seq) analysis. Combined with bulk RNA-seq data, we classified patients into two clusters and found that this classification was closely correlated with patient prognosis and immunotherapeutic responses. Based on machine learning, we identified a prognostic risk model composed of 14 DRGs, including BTG2, CDKN1A, COL6A1, CPM, CYB5D2, FOSB, ID2, ISG15, PLCG2, SECISBP2, SOCS3, TES, ZBTB16, and ZNF704, to predict the survival rate of patients and then constructed a nomogram model integrating clinicopathological characteristics and risk score for clinical practice. In the study of immune checkpoints, we found that patients in the high-risk group had a disposition to get worse prognosis and better effects of immune checkpoint blocking therapies. Finally, we found the expression level of model DRGs was associated with a tumor-immune microenvironment (TIME) pattern and the response of 83 compounds or inhibitors was significantly different in the two risk groups. In a word, our study highlights the potential contribution of cell differentiation in prognosis judgment and immunotherapy response and offers promising therapeutic options for ccRCC patients.

Bioinformatics analysis of immune-related prognostic genes and immunotherapy in renal clear cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is an immunogenic tumor, and investigating the immunorelated genes is essential. To investigate the immunoprognostic genes of ccRCC, we analyzed the data assimilated from a public database (The Cancer Genome Atlas (TCGA) database and the gene expression omnibus (GEO) database) using bioinformatics. Then, an immunoprognosis model was constructed to identify four hub genes with moderate predictive values for the prognosis of ccRCC patients. These four genes were associated with the prognosis of ccRCC patients based on Oncomine and Gena Expression Profiling Interactive Analysis (GEPIA) databases. The correlation analysis between the immune infiltrate, immune checkpoints, and immunotherapy and this immunoprognosis model showed that immune infiltration could predict the immunotherapy effects. We also conducted a quantitative real-time polymerase chain reaction analysis and found that the expressions of three hub genes were associated with tumor progression (P<0.1). In conclusion, four genes that may serve as potential biomarkers in ccRCC were identified with respect to prognosis.

A novel prognostic index of stomach adenocarcinoma based on immunogenomic landscape analysis and immunotherapy options

Stomach adenocarcinoma (STAD) is one of the most common malignant tumors worldwide. In this study, we attempted to construct a valid immune-associated gene prognostic index risk model that can predict the survival of patients with STAD and the efficacy of immune checkpoint inhibitors (ICIs) treatment. Transcriptome, clinical, and gene mutational data were obtained from the TCGA database. Immune-related genes were downloaded from the ImmPort and InnateDB databases. A total of 493 immune-related genes were identified to be enriched in functions associated with immune response, as well as in immune and tumor-related pathways. Further, 36 candidate genes related to the overall survival (OS) of STAD were obtained by weighted gene co-expression network analysis (WGCNA). Next, based on a Cox regression analysis, we constructed an immune-associated gene prognostic index (IAGPI) risk model based on eight genes, which was verified using the GEO STAD cohort. The patients were divided into two subsets according to their risk score. Patients in the low-risk group had better OS than those in the high-risk group. In the low-risk group, there were more CD8, activated memory CD4, and follicular helper T cells, and M1 macrophages, whereas monocytes, M2 macrophages, eosinophils, and neutrophils were more abundant in the high-risk group. The patients in the low-risk group were more sensitive to ICIs therapy. The IAGPI risk model can precisely predict the prognosis, reflect the tumor immune microenvironment, and predict the efficacy of ICIs therapy in patients with STAD.

ISG15 as a prognostic biomarker in solitary fibrous tumour

Background

Solitary fibrous tumour (SFT) is a rare mesenchymal malignancy that lacks robust prognostic and predictive biomarkers. Interferon-stimulated gene 15 (ISG15) is a ubiquitin-like modifier, associated with tumour progression, and with poor survival of SFT patients, as previous published by our group. Here, we describe the role of ISG15 in the biology of this rare tumour.

Methods

ISG15 expression was assessed by immunohistochemistry in tissue microarrays from SFT patients and tested for correlation with progression-free survival and overall survival (OS). The effects of ISG15 knockdown or induction were investigated for cancer stem cell (CSC) characteristics and for drug sensitivity in unique in vitro models of SFT.

Results

The prognostic value of ISG15 for OS was validated at protein level in malignant SFT patients, prospectively treated with pazopanib and enrolled in GEIS-32 trial. In SFT in vitro models, ISG15 knockdown lead to a decrease in the expression of CSC-related genes, including SOX2, NANOG, ALDH1A1, ABCB1 and ABCC1. Likewise, ISG15 downregulation decreased the clonogenic/ tumoursphere-forming ability of SFT cells, while enhancing apoptotic cell death after doxorubicin, pazopanib or trabectedin treatment in 3D cell cultures. The regulation of CSC-related genes by ISG15 was confirmed after inducing its expression with interferon-β1; ISG15 induction upregulated 1.28- to 451-fold the expression of CSC-associated genes.

Conclusions

ISG15 is a prognostic factor in malignant SFT, regulating the expression of CSC-related genes and CSCs maintenance. Our results suggest that ISG15 could be a novel therapeutic target in SFT, which could improve the efficacy of the currently available treatments.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00018-022-04454-4.

Effects and Prognostic Values of Circadian Genes CSNK1E/GNA11/KLF9/THRAP3 in Kidney Renal Clear Cell Carcinoma via a Comprehensive Analysis

Kidney renal clear cell carcinoma (KIRC) is one of the most prevalent and deadly types of renal cancer in adults. Recent research has identified circadian genes as being involved in the development and progression of KIRC by altering their expression. This study aimed to identify circadian genes that are differentially expressed in KIRC and assess their role in KIRC progression. In KIRC, there were 553 differentially expressed rhythm genes (DERGs), with 300 up-regulated and 253 down-regulated DERGs. Functional enrichment analyses showed that DERGs were greatly enriched in the circadian rhythm and immune response pathways. Survival analyses indicated that higher expression levels of CSNK1E were related to shorter overall survival of KIRC patients, whereas lower expression levels of GNA11, KLF9, and THRAP3 were associated with shorter overall survival of KIRC patients. Through cell assay verification, the mRNA level of CSNK1E was significantly up-regulated, whereas the mRNA levels of GNA11, KLF9, and THRAP3 were dramatically down-regulated in KIRC cells, which were consistent with the bioinformatics analysis of KIRC patient samples. Age, grade, stage, TM classification, and CSNK1E expression were all shown to be high-risk variables, whereas GNA11, KLF9, and THRAP3 expression were found to be low-risk factors in univariate Cox analyses. Multivariate Cox analyses showed that CSNK1E and KLF9 were also independently related to overall survival. Immune infiltration analysis indicated that the proportion of immune cells varied greatly between KIRC tissues and normal tissue, whereas CSNK1E, GNA11, KLF9, and THRAP3 expression levels were substantially linked with the infiltration abundance of immune cells and immunological biomarkers. Moreover, interaction networks between CSNK1E/GNA11/KLF9/THRAP3 and immune genes were constructed to explore the stream connections. The findings could help us better understand the molecular mechanisms of KIRC progression, and CSNK1E/GNA11/KLF9/THRAP3 might be used as molecular targets for chronotherapy in KIRC patients in the near future.

A novel immune-related RNA-binding proteins signature to predict survival and therapeutic responses in prostate cancer

BACKGROUND

The present study aims to identify immune-related RBPs signature to predict prognosis and therapy response in prostate cancer.

METHODS

Differentially expressed RBPs were compared and visualized using R packages. Immune-related RBPs were selected by Pearson correlation analysis. The prognostic immune-related RBPs were identified using the Kaplan-Meier method and LASSO regression. A multivariable Cox regression model was used to construct immune-related RBPs signature.

RESULTS

We constructed a prognostic predictive risk model of prostate cancer containing ten immune-related RBP genes. We found that high-risk prostate cancer patients presented poorer prognosis, higher tumor immune cell infiltration, higher rates of genomic alterations, and were more sensitive to targeted and immunotherapy than the low-risk group.

CONCLUSIONS

The immune-related RBPs' signature is an independent prognostic marker that could help screen patients with advanced prostate cancer who are better suited for targeted and immunotherapy.

Construction and validation of a prognostic model of RNA binding proteins in clear cell renal carcinoma

BACKGROUND

The dysfunction of RNA binding proteins (RBPs) is associated with various inflammation and cancer. The occurrence and progression of tumors are closely related to the abnormal expression of RBPs. There are few studies on RBPs in clear cell renal carcinoma (ccRCC), which allows us to explore the role of RBPs in ccRCC.

METHODS

We obtained the gene expression data and clinical data of ccRCC from the Cancer Genome Atlas (TCGA) database and extracted all the information of RBPs. We performed differential expression analysis of RBPs. Risk model were constructed based on the differentially expressed RBPs (DERBPs). The expression levels of model markers were examined by reverse transcription-quantitative PCR (RT-qPCR) and analyzed for model-clinical relevance. Finally, we mapped the model's nomograms to predict the 1, 3 and 5-year survival rates for ccRCC patients.

RESULTS

The results showed that the five-year survival rate for the high-risk group was 40.2% (95% CI = 0.313 ~ 0.518), while the five-year survival rate for the low-risk group was 84.3% (95% CI = 0.767 ~ 0.926). The ROC curves (AUC = 0.748) also showed that our model had stable predictive power. Further RT-qPCR results were in accordance with our analysis (p < 0.05). The results of the independent prognostic analysis showed that the model could be an independent prognostic factor for ccRCC. The results of the correlation analysis also demonstrated the good predictive ability of the model.

CONCLUSION

In summary, the 4-RBPs (EZH2, RPL22L1, RNASE2, U2AF1L4) risk model could be used as a prognostic indicator of ccRCC. Our study provides a possibility for predicting the survival of ccRCC.

A Novel Machine Learning 13-Gene Signature: Improving Risk Analysis and Survival Prediction for Clear Cell Renal Cell Carcinoma Patients

Simple Summary

Clear cell renal cell carcinoma is a type of kidney cancer which comprises the majority of all renal cell carcinomas. Many efforts have been made to identify biomarkers which could help healthcare professionals better treat this kind of cancer. With extensive public data available, we conducted a machine learning study to determine a gene signature that could indicate patient survival with high accuracy. Through the min-Redundancy and Max-Relevance algorithm we generated a signature of 13 genes highly correlated with patient outcomes. These findings reveal potential strategies for personalized medicine in the clinical practice.

Abstract

Patients with clear cell renal cell carcinoma (ccRCC) have poor survival outcomes, especially if it has metastasized. It is of paramount importance to identify biomarkers in genomic data that could help predict the aggressiveness of ccRCC and its resistance to drugs. Thus, we conducted a study with the aims of evaluating gene signatures and proposing a novel one with higher predictive power and generalization in comparison to the former signatures. Using ccRCC cohorts of the Cancer Genome Atlas (TCGA-KIRC) and International Cancer Genome Consortium (ICGC-RECA), we evaluated linear survival models of Cox regression with 14 signatures and six methods of feature selection, and performed functional analysis and differential gene expression approaches. In this study, we established a 13-gene signature (AR, AL353637.1, DPP6, FOXJ1, GNB3, HHLA2, IL4, LIMCH1, LINC01732, OTX1, SAA1, SEMA3G, ZIC2) whose expression levels are able to predict distinct outcomes of patients with ccRCC. Moreover, we performed a comparison between our signature and others from the literature. The best-performing gene signature was achieved using the ensemble method Min-Redundancy and Max-Relevance (mRMR). This signature comprises unique features in comparison to the others, such as generalization through different cohorts and being functionally enriched in significant pathways: Urothelial Carcinoma, Chronic Kidney disease, and Transitional cell carcinoma, Nephrolithiasis. From the 13 genes in our signature, eight are known to be correlated with ccRCC patient survival and four are immune-related. Our model showed a performance of 0.82 using the Receiver Operator Characteristic (ROC) Area Under Curve (AUC) metric and it generalized well between the cohorts. Our findings revealed two clusters of genes with high expression (SAA1, OTX1, ZIC2, LINC01732, GNB3 and IL4) and low expression (AL353637.1, AR, HHLA2, LIMCH1, SEMA3G, DPP6, and FOXJ1) which are both correlated with poor prognosis. This signature can potentially be used in clinical practice to support patient treatment care and follow-up.

Identification of Hypoxia-Immune-Related Gene Signatures and Construction of a Prognostic Model in Kidney Renal Clear Cell Carcinoma

Introduction: Kidney renal clear cell carcinoma (KIRC), a kind of malignant disease, is a severe threat to public health. Tracking the information of tumor progression and conducting a related dynamic prognosis model are necessary for KIRC. It is crucial to identify hypoxia-immune-related genes and construct a prognostic model due to immune interaction and the influence of hypoxia in the prognosis of patients with KIRC. Methods: The hypoxia and immune status of KIRC patients were identified by utilizing t-SNE and ImmuCellAI for gene expression data. COX and Lasso regression were used to identify some hypoxia-immune-related signature genes and further construct a prognostic risk model based on these genes. Internal and external validations were also conducted to construct a prognostic model. Finally, some potentially effective drugs were screened by the CMap dataset. Results: We found that high-hypoxia and low-immune status tend to induce poor overall survival (OS). Six genes, including PLAUR, UCN, PABPC1L, SLC16A12, NFE2L3, and KCNAB1, were identified and involved in our hypoxia-immune-related prognostic risk model. Internal verification showed that the area under the curve (AUC) for the constructed models for 1-, 3-, 4-, and 5-year OS were 0.768, 0.754, 0.775, and 0.792, respectively. For the external verification, the AUC for 1-, 3-, 4-, and 5-year OS were 0.768, 0.739, 0.763, and 0.643 respectively. Furthermore, the decision curve analysis findings demonstrated excellent clinical effectiveness. Finally, we found that four drugs (including vorinostat, fludroxycortide, oxolinic acid, and flutamide) might be effective and efficient in alleviating or reversing the status of severe hypoxia and poor infiltration of immune cells. Conclusion: Our constructed prognostic model, based on hypoxia-immune-related genes, has excellent effectiveness and clinical application value. Moreover, some small-molecule drugs are screened to alleviate severe hypoxia and poor infiltration of immune cells.

TIMP1 Indicates Poor Prognosis of Renal Cell Carcinoma and Accelerates Tumorigenesis via EMT Signaling Pathway

Renal cell carcinoma (RCC) is one of the most common malignancies in the urinary system. The mortality of advanced RCC remains high despite advances in systemic therapy of RCC. Considering the misdiagnosis of early-stage RCC, the identification of effective biomarkers is of great importance. Tissue inhibitor matrix metalloproteinase 1 (TIMP1), which belongs to TIMP gene family, is a natural inhibitor of the matrix metalloproteinases (MMPs). In this study, we found TIMP1 was significantly up-regulated in cell lines and RCC tissues. Kaplan-Meier analysis revealed that high expression of TIMP1 indicated a poor prognosis. Multivariate analysis further indicated that TIMP1 overexpression was an independent prognostic factor of RCC patients. Furthermore, knockdown of TIMP1 in vitro suppressed the proliferation, migration, and invasion of RCC cells, while upregulating TIMP1 accelerated the proliferation, migration, and invasion of RCC cells. In addition, we also found that TIMP1 prompted the progression of RCC via epithelial-to-mesenchymal transition (EMT) signaling pathway. In conclusion, the present results suggested that TIMP1 indicated poor prognosis of renal cell carcinoma and could serve as a potential diagnostic and prognostic biomarker for RCC.

Prognostic value and underlying mechanism of autophagy-related genes in bladder cancer

Bladder cancer (BLCA) is the most common malignancy whose early diagnosis can ensure a better prognosis. However, the predictive accuracy of commonly used predictors, including patients’ general condition, histological grade, and pathological stage, is insufficient to identify the patients who need invasive treatment. Autophagy is regarded as a vital factor in maintaining mitochondrial function and energy homeostasis in cancer cells. Whether autophagy-related genes (ARGs) can predict the prognosis of BLCA patients deserves to be investigated. Based on BLCA data retrieved from the Cancer Genome Atlas and ARGs list obtained from the Human Autophagy Database website, we identified prognosis-related differentially expressed ARGs (PDEARGs) through Wilcox text and constructed a PDEARGs-based prognostic model through multivariate Cox regression analysis. The predictive accuracy, independent forecasting capability, and the correlation between present model and clinical variables or tumor microenvironment were evaluated through R software. Enrichment analysis of PDEARGs was performed to explore the underlying mechanism, and a systematic prognostic signature with nomogram was constructed by integrating clinical variables and the aforementioned PDEARGs-based model. We found that the risk score generated by PDEARGs-based model could effectively reflect deteriorated clinical variables and tumor-promoting microenvironment. Additionally, several immune-related gene ontology terms were significantly enriched by PDEARGs, which might provide insights for present model and propose potential therapeutic targets for BLCA patients. Finally, a systematic prognostic signature with promoted clinical utility and predictive accuracy was constructed to assist clinician decision. PDEARGs are valuable prognostic predictors and potential therapeutic targets for BLCA patients.

External Validation of the Prognostic Value of an Immune-Associated Gene Panel for Clear Cell Renal Cell Carcinomas

Clear cell renal cell carcinomas (ccRCCs) are highly immune infiltrates, and many of them respond to immunotherapy with checkpoint inhibitors including anti-PD-L1 or anti-PD1 agents. However, the effect of immune genes on clinical outcomes in ccRCCs has not been fully studied. Here, we show in this study that an immune-associated gene panel has a prognostic value for clear cell renal cell carcinomas. We performed single-sample gene set enrichment analysis (ssGSEA) and cell type identification by estimating subsets of RNA transcripts (CIBERSORT) algorithms on patient-matched normal renal and RCC tissues to characterize two immunophenotypes and immunological characteristic subpopulations. Furthermore, LASSO Cox regression was applied to develop a novel prognosis-associated model for ccRCC patients based on an immune-gene panel. The results were verified by the Gene Expression Omnibus (GEO) dataset and coordinated with the clinicopathological characteristics of ccRCCs, along with genomic signatures. Finally, based on the above perspectives, we generated a nomogram with a high prognostic efficiency for ccRCC patients. Overall, this study offers a unique perspective that can contribute to improving the accuracy of prognosis prediction and treatment with immunotherapy.

Immunological perspective on the malignant progression of renal clear cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is the most common malignancy affecting the kidneys, accounting for approximately 75% of all kidney tumors. Recently, the impact of immune response, immunotherapy, and immune-related genes (IRGs) on tumor development has received much attention. This study sought to establish a reliable immunological signature and further explore whether this signature has prognostic significance in ccRCC patients.

Methods

Differentially expressed IRGs in 611 patients with diagnosis of ccRCC from The Cancer Genome Atlas (TCGA) were analyzed along with the corresponding survival time and disease clinical data. Survival analysis, selection operator Cox analysis, and minimum absolute shrinkage were applied to establish an IRG prognostic signature (IRGPS). The expression levels of relevant genes were detected by real-time quantitative PCR. A Nomogram was used to explore the possible impact of the IRGPS on the immune system, prognosis, and metastasis, and the associated mechanisms were explored through functional enrichment analysis.

Results

An IRGPS was established based on eight prognostic IRGs and was found to be closely associated with immune levels, metastasis, and prognosis. The IRGPS was determined to be a valid predictor of the efficacy of immune checkpoint inhibitors (ICIs). Three Nomograms based on the IRGPS and other clinical features were developed and could effectively predict prognosis, distant metastasis, and lymph node metastasis in patients with ccRCC.

Conclusions

The IRGPS constructed in this study serves as a tool for assessing immune status, developing individualized treatment regimens, and predicting prognosis in patients with ccRCC.

Emerging Role of Neuropilin-1 and Angiotensin-Converting Enzyme-2 in Renal Carcinoma-Associated COVID-19 Pathogenesis

Neuropilin-1 (NRP1) is a recently identified glycoprotein that is an important host factor for SARS-CoV-2 infection. On the other hand, angiotensin-converting enzyme-2 (ACE2) acts as a receptor for SARS-CoV-2. Additionally, both NRP1 and ACE2 express in the kidney and are associated with various renal diseases, including renal carcinoma. Therefore, the expression profiles of NRP1 and ACE2 in kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP) patients from the various cancer databases were investigated along with their impact on patients’ survivability. In addition, coexpression analysis of genes involved in COVID-19, KIRC, and KIRP concerning NRP1 and ACE2 was performed. The results demonstrated that both t NRP1 and ACE2 expressions are upregulated in KIRC and KIRP compared to healthy conditions and are significantly correlated with the survivability rate of KIRC patients. A total of 128 COVID-19-associated genes are coexpressed, which are positively associated with NRP1 and ACE2 both in KIRC and KIRP. Therefore, it might be suggested that, along with the ACE2, high expression of the newly identified host factor NRP1 in renal carcinomas may play a vital role in the increased risk of SARS-CoV-2 infection and survivability of COVID-19 patients suffering from kidney cancers. The findings of this investigation will be helpful for further molecular studies and prevention and/or treatment strategies for COVID-19 patients associated with renal carcinomas.

Characterization of Molecular Heterogeneity Associated With Tumor Microenvironment in Clear Cell Renal Cell Carcinoma to Aid Immunotherapy

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer and has strong immunogenicity. A systematically investigation of the tumor microenvironment (TME) in ccRCC could contribute to help clinicians develop personalized treatment and facilitate clinical decision-making. In this study, we analyzed the immune-related subtype of ccRCC on the basis of immune-related gene expression data in The Cancer Genome Atlas (TCGA, N = 512) and E-MTAB-1980 (N = 101) dataset, respectively. As a result, two subtypes (C1 and C2) were identified by performing non-negative matrix factorization clustering. Subtype C1 was characterized by increased advance ccRCC cases and immune-related pathways. A higher immune score, stromal score, TMB value, Tumor Immune Dysfunction and Exclusion (TIDE) prediction score, and immune checkpoint genes expression level were also observed in C1. In addition, the C1 subtype might benefit from chemotherapy and immunotherapy. The patients in subtype C2 had more metabolism-related pathways, higher tumor purity, and a better prognosis. Moreover, some small molecular compounds for the treatment of ccRCC were identified between the two subtypes by using the Connectivity Map (CMap) database. Finally, we constructed and validated an immune-related (IR) score to evaluate immune modification individually. A high IR score corresponded to a favorable prognosis compared to a low IR score, while more advanced tumor stage and grade cases were enriched in the low IR score group. The two IR score groups also showed a distinct divergence among immune status, TME, and chemotherapy. The external validation dataset (E-MTAB-1980) and another immunotherapy cohort (IMvigor 210) demonstrated that patients in the high IR score group had a significantly prolonged survival time and clinical benefits compared to the low IR score group. Together, characterization of molecular heterogeneity and IR signature may help develop new insights into the TME of ccRCC and provide new strategies for personalized treatment.

A united risk model of 11 immune‑related gene pairs and clinical stage for prediction of overall survival in clear cell renal cell carcinoma patients

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cancer. Currently, we lack effective risk models for the prognosis of ccRCC patients. Given the significant role of cancer immunity in ccRCC, we aimed to establish a novel united risk model including clinical stage and immune-related gene pairs (IRGPs) to assess the prognosis. The gene expression profile and clinical data of ccRCC patients from The Cancer Genome Atlas and Arrayexpress were divided into training cohort (n = 381), validation cohort 1 (n = 156), and validation cohort 2 (n = 101). Through univariate Cox regression analysis and Least Absolute Shrinkage and Selection Operator analysis, 11 IRGPs were obtained. After further analysis, it was found that clinical stage could be an independent prognostic factor; hence, we used it to construct a united prognostic model with 11 IRGPs. Based on this model, patients were divided into high-risk and low-risk groups. In Kaplan–Meier analysis, a significant difference was observed in overall survival (OS) among all three cohorts (p < 0.001). The calibration curve revealed that the signature model is in high accordance with the observed values of each data cohort. The 1-year, 3-year, and 5-year receiver operating characteristic curves of each data cohort showed better performance than only IRGP signatures. The results of immune infiltration analysis revealed significantly (p < 0.05) higher abundance of macrophages M0, T follicular helper cells, and other tumor infiltrating cells. In summary, we successfully established a united prognostic risk model, which can effectively assess the OS of ccRCC patients.

Integrated Analysis of the Roles of RNA Binding Proteins and Their Prognostic Value in Clear Cell Renal Cell Carcinoma

Methods

We downloaded the RNA sequencing data of ccRCC from the Cancer Genome Atlas (TCGA) database and identified differently expressed RBPs in different tissues. In this study, we used bioinformatics to analyze the expression and prognostic value of RBPs; then, we performed functional analysis and constructed a protein interaction network for them. We also screened out some RBPs related to the prognosis of ccRCC. Finally, based on the identified RBPs, we constructed a prognostic model that can predict patients' risk of illness and survival time. Also, the data in the HPA database were used for verification.

Results

In our experiment, we obtained 539 ccRCC samples and 72 normal controls. In the subsequent analysis, 87 upregulated RBPs and 38 downregulated RBPs were obtained. In addition, 9 genes related to the prognosis of patients were selected, namely, RPL36A, THOC6, RNASE2, NOVA2, TLR3, PPARGC1A, DARS, LARS2, and U2AF1L4. We further constructed a prognostic model based on these genes and plotted the ROC curve. This ROC curve performed well in judgement and evaluation. A nomogram that can judge the patient's life span is also made.

Conclusion

In conclusion, we have identified differentially expressed RBPs in ccRCC and carried out a series of in-depth research studies, the results of which may provide ideas for the diagnosis of ccRCC and the research of new targeted drugs.

Identification of a Competing Endogenous RNA Network Related to Immune Signature in Lung Adenocarcinoma

Background

The establishment of immunotherapy has led to a new era in oncotherapy. But the signature of immune-related genes (IRGs) in LUAD remains to be elucidated. Here we use integrated analysis to identify IRGs roles in immune signature and detect their relationship with competing endogenous RNA (ceRNA) networks in LUAD progression.

Methods

By analyzing the RNA-seq data from different platforms, we recognized the differentially expressed genes (DEGs) of each platform and screened out the top 20 hub IRGs related to immune responses. Then, we applied the CIBERSORT algorithm to explore the landscape of tumor-infiltrating immune cells (TILs) in LUAD and their connection with hub genes. Next, we predicted and validated the upstream miRNAs and lncRNAs according to their expression and prognostic roles. Finally, we constructed and validated an immune-related ceRNA network by co-expression analysis.

Results

A total of 71 IRGs were identified among 248 DEGs, which play key roles in immune responses. CIBERSORT analysis showed that six hub genes were closely related to TILs, such as SPP1 and naive B cells (R = −0.17), TEK and resting mast cells (R = 0.37). Stepwise prediction and validation from mRNA to lncRNA, including 6 hub genes, 5 miRNAs, and 9 lncRNAs, were applied to construct a ceRNA network. Ultimately, we confirmed the TMPO-AS1/miR-126-5p/SPP1 and CARD8-AS1/miR-21-5p/TEK as immune-related ceRNA networks in LUAD progression.

Conclusion

We elucidated two immune-related ceRNA networks in LUAD progression, which can be considered as immunotherapy targets for this disease.

A new thinking: extended application of genomic selection to screen multiomics data for development of novel hypoxia-immune biomarkers and target therapy of clear cell renal cell carcinoma

Increasing evidences show the clinical significance of the interaction between hypoxia and immune in clear cell renal cell carcinoma (ccRCC) microenvironment. However, reliable prognostic signatures based on a combination of hypoxia and immune have not been well established. Moreover, many studies have only used RNA-seq profiles to screen the prognosis feature of ccRCC. Presently, there is no comprehensive analysis of multiomics data to mine a better one. Thus, we try and get it. First, t-SNE and ssGSEA analysis were used to establish tumor subtypes related to hypoxia-immune, and we investigated the hypoxia-immune-related differences in three types of genetic or epigenetic characteristics (gene expression profiles, somatic mutation, and DNA methylation) by analyzing the multiomics data from The Cancer Genome Atlas (TCGA) portal. Additionally, a four-step strategy based on lasso regression and Cox regression was used to construct a satisfying prognostic model, with average 1-year, 3-year and 5-year areas under the curve (AUCs) equal to 0.806, 0.776 and 0.837. Comparing it with other nine known prognostic biomarkers and clinical prognostic scoring algorithms, the multiomics-based signature performs better. Then, we verified the gene expression differences in two external databases (ICGC and SYSU cohorts). Next, eight hub genes were singled out and seven hub genes were validated as prognostic genes in SYSU cohort. Furthermore, it was indicated high-risk patients have a better response for immunotherapy in immunophenoscore (IPS) analysis and TIDE algorithm. Meanwhile, estimated by GDSC and cMAP database, the high-risk patients showed sensitive responses to six chemotherapy drugs and six candidate small-molecule drugs. In summary, the signature can accurately predict the prognosis of ccRCC and may shed light on the development of novel hypoxia-immune biomarkers and target therapy of ccRCC.

A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a disease with a high incidence and a poor prognosis. Growing amounts of evidence have shown that the immune system plays a critical role in the biological processes of HCC such as progression, recurrence, and metastasis, and some have discussed using it as a weapon against a variety of cancers. However, the impact of immune-related genes (IRGs) on the prognosis of HCC remains unclear.

METHODS

Based on The Cancer Gene Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort) datasets, we integrated the ribonucleic acid (RNA) sequencing profiles of 424 HCC patients with IRGs to calculate immune-related differentially expressed genes (DEGs). Survival analysis was used to establish a prognostic model of survival- and immune-related DEGs. Based on genomic and clinicopathological data, we constructed a nomogram to predict the prognosis of HCC patients. Gene set enrichment analysis further clarified the signalling pathways of the high-risk and low-risk groups constructed based on the IRGs in HCC. Next, we evaluated the correlation between the risk score and the infiltration of immune cells, and finally, we validated the prognostic performance of this model in the GSE14520 dataset.

RESULTS

A total of 100 immune-related DEGs were significantly associated with the clinical outcomes of patients with HCC. We performed univariate and multivariate least absolute shrinkage and selection operator (Lasso) regression analyses on these genes to construct a prognostic model of seven IRGs (Fatty Acid Binding Protein 6 (FABP6), Microtubule-Associated Protein Tau (MAPT), Baculoviral IAP Repeat Containing 5 (BIRC5), Plexin-A1 (PLXNA1), Secreted Phosphoprotein 1 (SPP1), Stanniocalcin 2 (STC2) and Chondroitin Sulfate Proteoglycan 5 (CSPG5)), which showed better prognostic performance than the tumour/node/metastasis (TNM) staging system. Moreover, we constructed a regulatory network related to transcription factors (TFs) that further unravelled the regulatory mechanisms of these genes. According to the median value of the risk score, the entire TCGA cohort was divided into high-risk and low-risk groups, and the low-risk group had a better overall survival (OS) rate. To predict the OS rate of HCC, we established a gene- and clinical factor-related nomogram. The receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve showed that this model had moderate accuracy. The correlation analysis between the risk score and the infiltration of six common types of immune cells showed that the model could reflect the state of the immune microenvironment in HCC tumours.

CONCLUSION

Our IRG prognostic model was shown to have value in the monitoring, treatment, and prognostic assessment of HCC patients and could be used as a survival prediction tool in the near future.

Construction of a prognostic value model in papillary renal cell carcinoma by immune-related genes

Papillary renal cell carcinoma (PRCC) is the second most common type of renal carcinoma following clear cell renal cell carcinoma, and the role of immune-related genes (IRGs) in tumorigenesis and metastasis is evident; its prognostic value in PRCC remains unclear. In this study, we downloaded the gene expression profiles and clinical data of patients with PRCC from The Cancer Genome Atlas (TCGA) database and obtained IRGs from the ImmPort database. A total of 371 differentially expressed IRGs (DEIRGs) were discovered between PRCC and normal kidney tissues. Prognostic DEIRGs (PDEIRGs) were identified by univariate Cox regression analysis. Then, we screened the four most representative PDEIRGs (IL13RA2, CCL19, BIRC5, and INHBE) and used them to construct a risk model to predict the prognosis of patients with PRCC. This model precisely stratified survival outcome and accurately identified mutation burden in PRCC. Thus, our results suggest that these four PDEIRGs are available prognostic predictors for PRCC. They could be used to assess the prognosis and to guide individualized treatments for patients with PRCC.

Identification of a Novel Protein-Based Signature to Improve Prognosis Prediction in Renal Clear Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignant adult kidney cancer, and its incidence and mortality are not optimistic. It is well known that tumor-related protein markers play an important role in cancer detection, prognosis prediction, or treatment selection, such as carcinoembryonic antigen (CEA), programmed cell death 1 (PD-1), programmed cell death 1 ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen 4 (CTLA-4), so a comprehensive analysis was performed in this study to explore the prognostic value of protein expression in patients with ccRCC.

Materials and Methods

Protein expression data were obtained from The Cancer Proteome Atlas (TCPA), and clinical information were downloaded from The Cancer Genome Atlas (TCGA). We selected 445 patients with complete information and then separated them into a training set and testing set. We performed univariate, least absolute shrinkage and selection operator (LASSO) Cox analyses to find prognosis-related proteins (PRPs) and constructed a protein signature. Then, we used stratified analysis to fully verify the prognostic significance of the prognostic-related protein signature score (PRPscore). Besides, we also explored the differences in immunotherapy response and immune cell infiltration level in high and low score groups. The consensus clustering analysis was also performed to identify potential cancer subgroups.

Results

From the training set, a total of 233 PRPs were selected, and a seven-protein signature was constructed, including ACC1, AR, MAPK, PDK1, PEA15, SYK, and BRAF. Based on the PRPscore, patients could be divided into two groups with significantly different overall survival rates. Univariate and multivariate Cox regression analyses proved that this signature was an independent prognostic factor for patients (P < 0.001). Moreover, the signature showed a high ability to distinguish prognostic outcomes among subgroups, and the low score group had a better prognosis (P < 0.001) and better immunotherapy response (P = 0.003) than the high score group.

Conclusion

We constructed a novel protein signature with robust predictive power and high clinical value. This will help to guide the disease management and individualized treatment of ccRCC patients.

The Diagnostic and Immunotherapeutic Value of CD248 in Renal Cell Carcinoma

Background: Renal cell carcinoma (RCC) is the most common malignancy in the urinary system. Despite substantial improvements in available treatment options, the survival outcome of advanced RCC is unsatisfactory. Identifying novel biomarkers to assist in early diagnosis and to screen patients who are sensitive to immunotherapy would be beneficial. CD248 is a promising candidate that deserves to be investigated.

Methods: The Cancer Genome Atlas (TCGA) data set and clinical specimens were adopted to analyze the expression of CD248 between normal and tumor tissues. Univariate and multivariate Cox regression analyses were employed to identify independent prognostic factors and construct a CD248-based prognostic signature. The correlation among the present signature, tumor-infiltrating immune cells (TIICs), the tumor mutation burden (TMB), and immunomodulatory molecules was evaluated. The weighted gene co-expression network analysis (WGCNA), the enrichment analysis, and the miRNA correlation analysis were performed to explore the underlying mechanism of CD248 in the progression of RCC.

Results: The overexpression of CD248 in RCC was related to a poor prognosis, and a CD248-based prognostic signature could precisely stratify patients with RCC with different survival outcomes regardless of the training or testing cohort. The present signature could reflect the immunosuppressive landscape of RCC (i.e., increased infiltration of regulatory T cells and upregulated immune checkpoints), accompanied by deteriorated clinicopathologic indexes. The TMB and immunostimulatory molecules expression also increased with the risk score generated from the present signature. CD248 co-expressed gene sets were identified through the WGCNA algorithm, and several immunosuppressive Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were significantly enriched. The result of CD248-correlated miRNA further emphasized the importance of CD248 in RCC.

Conclusion: CD248 is a valuable biomarker to improve the diagnostic and therapeutic efficiency of RCC. The immunosuppressive effect of CD248 co-expressed genes may provide insight for the present study, and miRNA would help to reveal the mechanism of the expressive regulation of CD248.

Identification of a prognostic signature based on immune-related genes in bladder cancer

Bladder cancer (BLCA) has a high incidence and recurrence rate, and the effect of immunotherapy varies from person to person. Immune-related genes (IRGs) have been shown to be associated with immunotherapy and prognosis in many other cancers, but their role in immunogenic BLCA is less well defined. In this study, we constructed an eight-IRG risk model, which demonstrated strong prognostic and immunotherapeutic predictive power. The signature was significantly related to tumor clinicopathological characteristics, tumor class, immune cell infiltration and mutation status. Additionally, a nomogram containing the risk score and other potential risk factors could effectively predict the long-term overall survival probability of BLCA patients. The enriched mechanisms identified by gene set enrichment analysis suggested that the reason why this signature can accurately distinguish high- and low-risk populations may be closely related to the different degrees of innate immune response and T cell activation in different patients.

Artificial intelligence prediction model for overall survival of clear cell renal cell carcinoma based on a 21-gene molecular prognostic score system

We developed and validated a new prognostic model for predicting the overall survival in clear cell renal cell carcinoma (ccRCC) patients. In this study, artificial intelligence (AI) algorithms including random forest and neural network were trained to build a molecular prognostic score (mPS) system. Afterwards, we investigated the potential mechanisms underlying mPS by assessing gene set enrichment analysis, mutations, copy number variations (CNVs) and immune cell infiltration. A total of 275 prognosis-related genes were identified, which were also differentially expressed between ccRCC patients and healthy controls. We then constructed a universal mPS system that depends on the expression status of only 21 of these genes by applying AI-based algorithms. Then, the mPS were validated by another independent cohort and demonstrated to be applicable to ccRCC subsets. Furthermore, a nomogram comprising the mPS score and several independent variables was established and proved to effectively predict ccRCC patient prognosis. Finally, significant differences were identified regarding the pathways, mutated genes, CNVs and tumor-infiltrating immune cells among the subgroups of ccRCC stratified by the mPS system. The AI-based mPS system can provide critical prognostic prediction for ccRCC patients and may be useful to inform treatment and surveillance decisions before initial intervention.

Preclinical analysis of novel prognostic transcription factors and immune-related gene signatures for bladder cancer via TCGA-based bioinformatic analysis

Bladder cancer (BLCA) is a common malignancy of human urinary tract, whose prognosis is influenced by complex gene interactions. Immune response activity can act as a potential prognostic factor in BLCA. The present study established a prognostic model, based on the identification of tumor transcription factors (TFs) and immune-related genes (IRGs), and further explored their therapeutic potential in BLCA. The enrichment scores of 29 IRG sets, identified in The Cancer Genome Atlas BLCA tumor samples, were quantified by single-sample Gene Set Enrichment Analysis. The abundance of infiltrated immune cells in tumor tissues was determined using the Estimating Relative algorithm. Tumor-related TFs and IRGs signatures were retrieved using Least Absolute Shrinkage and Selection Operator Cox regression analysis. A prognostic gene network was built using Pearson's correlation analysis as a means of predicting the regulatory relationship between prognostic TFs and IRGs. A nomogram was devised to also predict the overall survival (OS) rate of patients with BLCA. Based on the Genomics of Drug Sensitivity in Cancer data, potential therapeutic drugs were identified upon analyzing the relationship between the expression level of prognostic genes and respective IC₅₀ values. In vitro experiments were implemented for further validation. Respective TF binding profiles were acquired from the JASPAR 2020 database. The elevated infiltration of CD8⁺ T Cells was correlated with an improved OS of patients with BLCA. An innovative prognostic model for BLCA was then constructed that composed of nine putative gene markers: CXCL13, prepronociceptin, microtubule-associated protein tau, major histocompatibility class I polypeptide-related sequence B, prostaglandin E2 receptor EP3 subtype, IL20RA, proepiregulin, early growth response protein 1 and FOS-related antigen 1 (FOSL1). Furthermore, a theoretical basis for the correlation between the prognostic TFs and IRGs was reported. For this, 10 potentially effective drugs targeting the TFs in the present model for patients with BLCA were identified. It was then verified that downregulation of FOSL1 can lead to an enhanced sensitivity of the TW37 in T24 bladder cancer cells. Overall, the present prognostic model demonstrated a robust capability of predicting OS of patients with BLCA. Hence, the gene markers identified could be applied for targeted therapies against BLCA.

A signature of seven immune-related genes predicts overall survival in male gastric cancer patients

Background

Gastric cancer (GC) has a high mortality rate and is one of the most fatal malignant tumours. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) associated with the prognosis of male GC.

Methods

RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analyses were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index and calibration curves. In addition, the gene expression dataset from the Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT.

Results

A total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified to be significantly associated with the overall survival (OS) of male GC patients. Survival analysis indicated that patients in the high-risk group exhibited a poor clinical outcome. The results of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had excellent predictive performance in both TCGA and validated cohorts. Besides, the results of tumour-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumour immune microenvironment.

Conclusions

Our study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

Immune-related genes with APA in microenvironment indicate risk stratification and clinical prognosis in grade II/III gliomas

Tumor microenvironment and alternative polyadenylation (APA) have drawn more attention in cancer research. However, their roles in grade II and III gliomas, termed as lower-grade glioma (LGG) in this study, remain to be fully elucidated. Here, we conducted this study and found that stromal and immune scores were elevated in higher grade and isocitrate dehydrogenase (IDH) wild-type glioma. Besides, higher stromal and immune scores indicated a poor prognosis in patients with LGG. APA events in immune-related genes were associated with overall survival, RNA expression, IDH mutation, and disease-free survival. Patients in the high-risk group had poor prognoses, and the risk score could be used to predict the survival rate. The risk score was positively correlated with the expression of immune checkpoints, inflammatory cytokines, and infiltrated immune cells. Moreover, risk stratification could predict the efficacy of radiotherapy and provide a reference for the treatment of grade III glioma. Our study revealed that immune-related genes with APA events in the microenvironment could predict risk stratification and clinical prognosis in patients with LGG.

The Clinical Significance of DNA Damage Repair Signatures in Clear Cell Renal Cell Carcinoma

DNA damage repair plays an important role in cancer’s initiation and progression, and in therapeutic resistance. The prognostic potential of damage repair indicators was studied in the case of clear cell renal cell carcinoma (ccRCC). Gene expression profiles of the disease were downloaded from cancer genome databases and gene ontology was applied to the DNA repair-related genes. Twenty-six differentially expressed DNA repair genes were identified, and regression analysis was used to identify those with prognostic potential and to construct a risk model. The model accurately predicted patient outcomes and distinguished among patients with different expression levels of immune evasion genes. The data indicate that DNA repair genes can be valuable for predicting the progression of clear cell renal cell carcinoma and the clinical benefits of immunotherapy.

Construction of an immune-related gene signature to predict survival and treatment outcome in gastric cancer

Immune cells have emerged as key regulators in the occurrence and development of multiple tumor types. However, it is unclear whether immune-related genes (IRGs) and the tumor immune microenvironment can predict prognosis for patients with gastric cancer (GC). The mRNA expression data in GC tissues (n = 368) were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs in patients with GC were determined using a computational difference algorithm. A prognostic signature was constructed using COX regression and random survival forest (RSF) analyses. In addition, datasets related to "gemcitabine resistance" and "trastuzumab resistance" (GSE58118 and GSE77346) were obtained for GEO database, and DEGs associated with drug-resistance were screened. Then, we analyzed correlations between gene expression and cancer immune infiltrates via Tumor Immune Estimation Resource (TIMER) site. The cBioportal database was used to analyze drug-resistant gene mutation status and survival. One hundred and fifty-five differentially expressed IRGs were screened between GC and normal tissues, and a prognostic signature consisting of four IRGs (NRP1, PPP3R1, IL17RA, and FGF16) was closely related to the overall survival (OS). According to cutoff value of risk score, patients were divided into high-risk and low-risk group. Patients in the high-risk group had shorter OS compared to the low-risk group in both the training (p < 0.0001) and testing sets (p = 0.0021). In addition, we developed a 5-IRGs (LGR6, DKK1, TNFRSF1B, NRP1, and CXCR4) signature which may participate in drug resistance processes in GC. Survival analysis showed that patients with drug-resistant gene mutations had shorter OS (p = 0.0459) and DFS (p < 0.001). We constructed four survival-related IRGs and five IRGs related to drug resistance which may contribute to predict the prognosis of GC.

Integral Analysis of the RNA Binding Protein-associated Prognostic Model for Renal Cell Carcinoma

RNA binding protein (RBPs) dysregulation has been reported in various malignant tumors and plays a pivotal role in tumor carcinogenesis and progression. However, the underlying mechanisms in renal cell carcinoma (RCC) are still unknown. In the present study, we performed a bioinformatics analysis using data from TCGA database to explore the expression and prognostic value of RBPs. We identified 125 differently expressed RBPs between tumor and normal tissue in RCC patients, including 87 upregulated and 38 downregulated RBPs. Eight RBPs (RPL22L1, RNASE2, RNASE3, EZH2, DDX25, DQX1, EXOSC5, DDX47) were selected as prognosis-related RBPs and used to construct a risk score model. In the risk score model, the high-risk subgroup had a poorer overall survival (OS) than the low-risk subgroup, and we divided the 539 RCC patients into two groups and conducted a time-dependent receiver operating characteristic (ROC) analysis to further test the prognostic ability of the eight hub RBPs. The area under the curve (AUC) of the ROC curve was 0.728 in train-group and 0.688 in test-group, indicating a good prognostic model. More importantly, we established a nomogram based on the selected eight RBPs. The eight selected RBPS have predictive value for RCC patients, with potential applications in clinical decision-making and individualized treatment.

A prognostic risk model based on immune-related genes predicts overall survival of patients with hepatocellular carcinoma

BACKGROUND AND AIMS

Hepatocellular carcinoma (HCC) is one of the most common heterogeneous tumors that occurs after chronic liver diseases and hepatitis virus infection. Immune-related genes (IRGs) and their ligands regulate the homeostasis of tumor microenvironment, which is essential for the treatment of HCC and its prognosis. This study aimed to investigate the clinical value of IRGs in predicting the prognosis of HCC.

METHODS

We downloaded RNA-seq data and clinical information from TCGA database. Samples were randomly divided into training cohort and testing cohort. The "limma" R package was performed to identify differentially expressed IRGs (DEIRGs) between HCC group and normal group. Prognostic DEIRGs (PDEIRGs) were obtained by univariate Cox analysis. LASSO and multivariate Cox analysis were used, and a prognostic risk model was constructed. In order to better demonstrate the clinical value of our model in predicting overall survival rate, a nomogram was constructed. To further investigate the molecular mechanism of our model, gene set enrichment analysis (GSEA) was performed.

RESULTS

Compared with the low-risk group, the high-risk group had a significantly worse prognosis. Moreover, our prognostic risk model can accurately stratify tumor grade and TNM stage. Importantly, in our model, not only immune checkpoint genes were well predicted, but also human leucocyte antigen-I molecules were revealed. GSEA suggested that "MAPK signaling pathway," "mTOR signaling pathway," "NOD like receptor signaling pathway," "Toll like receptor signaling pathway," "VEGF signaling pathway," "WNT signaling pathway" had significant correlations with the high-risk group.

CONCLUSION

Overall, our study showed that our prognostic risk model can be used to assess prognosis of HCC, which may provide a certain basis for the survival rate of patients with HCC.

Establishment of an immune-related gene pair model to predict colon adenocarcinoma prognosis

Background

Colon cancer is the most common type of gastrointestinal cancer and has high morbidity and mortality. Colon adenocarcinoma (COAD) is the main pathological type of colon cancer, and much evidence has supported the correlation between the prognosis of COAD and the immune system. The current study aimed to develop a robust prognostic immune-related gene pair (IRGP) model to estimate the overall survival of patients with COAD.

Methods

The gene expression profiles and clinical information of patients with colon adenocarcinoma were obtained from the TCGA and GEO databases and were divided into training and validation cohorts. Immune genes were selected that showed a significant association with prognosis.

Results

Among 1647 immune genes, a model with 17 IRGPs was built that was significantly associated with OS in the training cohort. In the training and validation datasets, the IRGP model divided patients into the high-risk group and low-risk group, and the prognosis of the high-risk group was significantly worse (P<0.001). Univariate and multivariate Cox proportional hazard analyses confirmed the feasibility of this model. Functional analysis confirmed that multiple tumor progression and stem cell growth-related pathways were upregulated in the high-risk groups. Regulatory T cells and macrophages M0 were significantly highly expressed in the high-risk group.

Conclusion

We successfully constructed an IRGP model that can predict the prognosis of COAD, providing new insights into the treatment strategy of COAD.

Supplementary information

Supplementary information accompanies this paper at 10.1186/s12885-020-07532-7.

Identification and prognostic value of metabolism-related genes in gastric cancer

Gastric cancer (GC) is one of the most commonly occurring cancers, and metabolism-related genes (MRGs) are associated with its development. Transcriptome data and the relevant clinical data were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases, and we identified 194 MRGs differentially expressed between GC and adjacent nontumor tissues. Through univariate Cox and lasso regression analyses we identified 13 potential prognostic differentially expressed MRGs (PDEMRGs). These PDEMRGs (CKMT2, ME1, GSTA2, ASAH1, GGT5, RDH12, NNMT, POLR1A, ACYP1, GLA, OPLAH, DCK, and POLD3) were used to build a Cox regression risk model to predict the prognosis of GC patients. Further univariate and multivariate Cox regression analyses showed that this model could serve as an independent prognostic parameter. Gene Set Enrichment Analysis showed significant enrichment pathways that could potentially contribute to pathogenesis. This model also revealed the probability of genetic alterations of PDEMRGs. We have thus identified a valuable metabolic model for predicting the prognosis of GC patients. The PDEMRGs in this model reflect the dysregulated metabolic microenvironment of GC and provide useful noninvasive biomarkers.

Identification of the Prognostic Value of Immune-Related Genes in Esophageal Cancer

Esophageal cancer (EC) is a serious malignant tumor, both in terms of mortality and prognosis, and immune-related genes (IRGs) are key contributors to its development. In recent years, immunotherapy for tumors has been widely studied, but a practical prognostic model based on immune-related genes (IRGs) in EC has not been established and reported. This study aimed to develop an immunogenomic risk score for predicting survival outcomes among EC patients. In this study, we downloaded the transcriptome profiling data and matched clinical data of EC patients from The Cancer Genome Atlas (TCGA) database and found 4,094 differentially expressed genes (DEGs) between EC and normal esophageal tissue (p < 0.05 and fold change >2). Then, the intersection of DEGs and the immune genes in the “ImmPort” database resulted in 303 differentially expressed immune-related genes (DEIRGs). Next, through univariate Cox regression analysis of DEIRGs, we obtained 17 immune genes related to prognosis. We detected nine optimal survival-associated IRGs (HSPA6, CACYBP, DKK1, EGF, FGF19, GAST, OSM, ANGPTL3, NR2F2) by using Lasso regression and multivariate Cox regression analyses. Finally, we used those survival-associated IRGs to construct a risk model to predict the prognosis of EC patients. This model could accurately predict overall survival in EC and could be used as a classifier for the evaluation of low-risk and high-risk groups. In conclusion, we identified a practical and robust nine-gene prognostic model based on immune gene dataset. These genes may provide valuable biomarkers and prognostic predictors for EC patients and could be further studied to help understand the mechanism of EC occurrence and development.

Immune-related genes play an important role in the prognosis of patients with testicular germ cell tumor

Background

Testicular cancer is a very common malignancy in young men. Although testicular cancer has a high cure rate, patients have a high long-term risk of secondary malignant tumors and cardiovascular disease. In addition, for patients resistant to traditional treatment methods, new treatment methods and methods for predicting prognosis are also urgently needed.

Methods

Gene expression profiles of 165 normal testicular tissues and 156 testicular germ cell tumor (TGCT) tissues from GTEx database and TCGA database were used to obtain differentially expressed genes (DEGs) in TGCT. Through the ImmPort database, we obtained immune-related genes (IRGs). Univariate Cox regression analysis was used to identify prognostic IRGs. A transcription factor regulatory network was constructed to clarify the possible regulatory mechanism for the differential expression of these IRGs. Multivariate Cox regression analysis was used to establish a prognostic model. Gene expression data and related survival data of 108 TCGT patients from GEO database were used for external validation. Survival analysis, receiver operating characteristic curves (ROC) curve analysis, independent prognostic analysis, principal component analysis (PCA) and clinical correlation analysis were performed to evaluate this model.

Results

Three hundred and thirty-three IRGs were differentially expressed between TGCT and normal testicular tissues. We established a prognostic model (riskScore) based on 5 risk genes (SEMA6B, SEMA3G, OBP2B, INSL6 and RETN). Whether in the training cohort, the testing cohort or the entire TCGA cohort, this model could accurately stratify patients with different survival outcomes. The prognostic value of riskScore and 5 risk genes was also confirmed in the GEO database. GSEA analysis showed that DEGs in patients with better prognosis were enriched in immune-related pathways, while DEGs in patients with poorer prognosis were enriched in cancer-related pathways and cardiovascular disease-related pathways. Finally, a new Nomogram with higher prognostic value was constructed to better predict the 1-year PFS, 3-year PFS and 5-year PFS of TCGT patients.

Conclusions

We successfully established an immune-related risk model with high prognostic value and created a new Nomogram. We found that different immune status in tumor microenvironment may be responsible for the different survival outcomes among TGCT patients.